{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:33.579097Z",
     "iopub.status.busy": "2021-04-15T10:15:33.568024Z",
     "iopub.status.idle": "2021-04-15T10:15:49.608877Z",
     "shell.execute_reply": "2021-04-15T10:15:49.607431Z"
    },
    "papermill": {
     "duration": 16.066602,
     "end_time": "2021-04-15T10:15:49.609041",
     "exception": false,
     "start_time": "2021-04-15T10:15:33.542439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import zoom\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras import backend as K\n",
    "from kerashypetune import KerasGridSearch\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:49.659311Z",
     "iopub.status.busy": "2021-04-15T10:15:49.648388Z",
     "iopub.status.idle": "2021-04-15T10:15:50.145864Z",
     "shell.execute_reply": "2021-04-15T10:15:50.144921Z"
    },
    "papermill": {
     "duration": 0.519984,
     "end_time": "2021-04-15T10:15:50.146093",
     "exception": false,
     "start_time": "2021-04-15T10:15:49.626109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43824, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>cbwd</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  year  month  day  hour  pm2.5  DEWP  TEMP    PRES  cbwd    Iws  Is  Ir  \\\n",
       "0   1  2010      1    1     0  129.0   -21 -11.0  1021.0     0   1.79   0   0   \n",
       "1   2  2010      1    1     1  129.0   -21 -12.0  1020.0     0   4.92   0   0   \n",
       "2   3  2010      1    1     2  129.0   -21 -11.0  1019.0     0   6.71   0   0   \n",
       "3   4  2010      1    1     3  129.0   -21 -14.0  1019.0     0   9.84   0   0   \n",
       "4   5  2010      1    1     4  129.0   -20 -12.0  1018.0     0  12.97   0   0   \n",
       "\n",
       "                 date  \n",
       "0 2010-01-01 00:00:00  \n",
       "1 2010-01-01 01:00:00  \n",
       "2 2010-01-01 02:00:00  \n",
       "3 2010-01-01 03:00:00  \n",
       "4 2010-01-01 04:00:00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ DATA ###\n",
    "\n",
    "df = pd.read_csv('BeijingPM2.csv')\n",
    "df['date'] = pd.to_datetime(df[['year','month','day','hour']])\n",
    "df['cbwd'] = df.cbwd.factorize()[0]\n",
    "df['pm2.5'] = df['pm2.5'].interpolate(method='linear', limit_direction='both')\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.187449Z",
     "iopub.status.busy": "2021-04-15T10:15:50.186689Z",
     "iopub.status.idle": "2021-04-15T10:15:50.214170Z",
     "shell.execute_reply": "2021-04-15T10:15:50.213211Z"
    },
    "papermill": {
     "duration": 0.052002,
     "end_time": "2021-04-15T10:15:50.214354",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.162352",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26304, 7) (8760, 7) (8760, 7)\n"
     ]
    }
   ],
   "source": [
    "### TRAIN, VALIDATION, TEST TEMPORAL SPLIT ###\n",
    "\n",
    "columns = ['DEWP','TEMP','PRES','Iws','Is','Ir','cbwd']\n",
    "\n",
    "y_train = df[df.year <= 2012]['pm2.5'].copy().values\n",
    "train_date = df[df.year <= 2012].date.values\n",
    "x_train = df[df.year <= 2012][columns].copy()\n",
    "\n",
    "y_val = df[df.year == 2013]['pm2.5'].copy().values\n",
    "val_date = df[df.year == 2013].date.values\n",
    "x_val = df[df.year == 2013][columns].copy()\n",
    "\n",
    "y_test = df[df.year == 2014]['pm2.5'].copy().values\n",
    "test_date = df[df.year == 2014].date.values\n",
    "x_test = df[df.year == 2014][columns].copy()\n",
    "\n",
    "print(x_train.shape, x_val.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.271662Z",
     "iopub.status.busy": "2021-04-15T10:15:50.270799Z",
     "iopub.status.idle": "2021-04-15T10:15:50.550485Z",
     "shell.execute_reply": "2021-04-15T10:15:50.551082Z"
    },
    "papermill": {
     "duration": 0.320732,
     "end_time": "2021-04-15T10:15:50.551247",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.230515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT TRAIN, VALIDATION, TEST ###\n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "plt.plot(train_date, y_train, label='train', c='blue')\n",
    "plt.plot(val_date, y_val, label='val', c='cyan')\n",
    "plt.plot(test_date, y_test, label='test', c='orange')\n",
    "plt.ylabel('PM2.5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.597165Z",
     "iopub.status.busy": "2021-04-15T10:15:50.594735Z",
     "iopub.status.idle": "2021-04-15T10:15:50.597865Z",
     "shell.execute_reply": "2021-04-15T10:15:50.598481Z"
    },
    "papermill": {
     "duration": 0.028614,
     "end_time": "2021-04-15T10:15:50.598621",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.570007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTION FOR SEQUENCES GENERATION ###\n",
    "\n",
    "sequence_length = 24*2\n",
    "\n",
    "def gen_sequence(id_df, seq_length, seq_cols):\n",
    "\n",
    "    data_matrix = id_df[seq_cols].values\n",
    "    num_elements = data_matrix.shape[0]\n",
    "\n",
    "    for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n",
    "        yield data_matrix[start:stop, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.646634Z",
     "iopub.status.busy": "2021-04-15T10:15:50.645643Z",
     "iopub.status.idle": "2021-04-15T10:15:50.772764Z",
     "shell.execute_reply": "2021-04-15T10:15:50.773213Z"
    },
    "papermill": {
     "duration": 0.155884,
     "end_time": "2021-04-15T10:15:50.773397",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.617513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26256, 48, 7) (8712, 48, 7) (8712, 48, 7)\n"
     ]
    }
   ],
   "source": [
    "### GENERATE SEQUENCES ###\n",
    "\n",
    "X_train, X_val, X_test = [], [], []\n",
    "\n",
    "for sequence in gen_sequence(x_train, sequence_length, columns):\n",
    "    X_train.append(sequence)\n",
    "    \n",
    "for sequence in gen_sequence(x_val, sequence_length, columns):\n",
    "    X_val.append(sequence)\n",
    "    \n",
    "for sequence in gen_sequence(x_test, sequence_length, columns):\n",
    "    X_test.append(sequence)\n",
    "    \n",
    "X_train, X_val, X_test = np.asarray(X_train), np.asarray(X_val), np.asarray(X_test)\n",
    "print(X_train.shape, X_val.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.817351Z",
     "iopub.status.busy": "2021-04-15T10:15:50.816650Z",
     "iopub.status.idle": "2021-04-15T10:15:50.821980Z",
     "shell.execute_reply": "2021-04-15T10:15:50.821454Z"
    },
    "papermill": {
     "duration": 0.029266,
     "end_time": "2021-04-15T10:15:50.822105",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.792839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTIONS FOR TARGET SCALING ###\n",
    "\n",
    "def scale_target(y, mean, std):\n",
    "    return (y - mean)/std\n",
    "\n",
    "def reverse_target(pred, mean, std): \n",
    "    return pred*std + mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.869836Z",
     "iopub.status.busy": "2021-04-15T10:15:50.866177Z",
     "iopub.status.idle": "2021-04-15T10:15:50.872769Z",
     "shell.execute_reply": "2021-04-15T10:15:50.873303Z"
    },
    "papermill": {
     "duration": 0.032132,
     "end_time": "2021-04-15T10:15:50.873453",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.841321",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26256,) (8712,) (8712,)\n"
     ]
    }
   ],
   "source": [
    "### SCALE TARGET ###\n",
    "\n",
    "mean_train = y_train.mean()\n",
    "std_train = y_train.std()\n",
    "\n",
    "y_train_seq = scale_target(y_train[sequence_length:], mean_train, std_train)\n",
    "y_val_seq = scale_target(y_val[sequence_length:], mean_train, std_train)\n",
    "y_test_seq = scale_target(y_test[sequence_length:], mean_train, std_train)\n",
    "\n",
    "print(y_train_seq.shape, y_val_seq.shape, y_test_seq.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:50.920642Z",
     "iopub.status.busy": "2021-04-15T10:15:50.919181Z",
     "iopub.status.idle": "2021-04-15T10:15:51.336147Z",
     "shell.execute_reply": "2021-04-15T10:15:51.335410Z"
    },
    "papermill": {
     "duration": 0.442967,
     "end_time": "2021-04-15T10:15:51.336336",
     "exception": false,
     "start_time": "2021-04-15T10:15:50.893369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### SCALE SEQUENCES ###\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train.reshape(-1,len(columns))).reshape(X_train.shape)\n",
    "X_val = scaler.transform(X_val.reshape(-1,len(columns))).reshape(X_val.shape)\n",
    "X_test = scaler.transform(X_test.reshape(-1,len(columns))).reshape(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:51.384190Z",
     "iopub.status.busy": "2021-04-15T10:15:51.383414Z",
     "iopub.status.idle": "2021-04-15T10:15:51.387741Z",
     "shell.execute_reply": "2021-04-15T10:15:51.387193Z"
    },
    "papermill": {
     "duration": 0.032116,
     "end_time": "2021-04-15T10:15:51.387900",
     "exception": false,
     "start_time": "2021-04-15T10:15:51.355784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### DEFINE MODEL ###\n",
    "\n",
    "def set_seed(seed):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    \n",
    "\n",
    "def get_model(params):\n",
    "    \n",
    "    set_seed(33)\n",
    "    \n",
    "    inp = Input(shape=(sequence_length, len(columns)))\n",
    "\n",
    "    x = GRU(params['units_gru'], return_sequences=True)(inp)\n",
    "    x = AveragePooling1D(2)(x)\n",
    "    x = Conv1D(params['units_cnn'], 3, \n",
    "               activation='relu', padding='same', \n",
    "               name='extractor')(x)\n",
    "    x = Flatten()(x)\n",
    "    out = Dense(1)(x)\n",
    "    \n",
    "    model = Model(inp, out)\n",
    "    model.compile(optimizer=Adam(lr=params['lr']), loss='mse')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:15:51.435174Z",
     "iopub.status.busy": "2021-04-15T10:15:51.434496Z",
     "iopub.status.idle": "2021-04-15T10:18:58.148480Z",
     "shell.execute_reply": "2021-04-15T10:18:58.147456Z"
    },
    "papermill": {
     "duration": 186.740807,
     "end_time": "2021-04-15T10:18:58.148708",
     "exception": false,
     "start_time": "2021-04-15T10:15:51.407901",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "16 trials detected for ('units_gru', 'units_cnn', 'lr', 'batch_size')\n",
      "\n",
      "***** (1/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 64, 'lr': 0.0001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00025: early stopping\n",
      "SCORE: 0.5623 at epoch 20\n",
      "\n",
      "***** (2/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 64, 'lr': 0.0001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00024: early stopping\n",
      "SCORE: 0.5856 at epoch 22\n",
      "\n",
      "***** (3/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 64, 'lr': 0.001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00008: early stopping\n",
      "SCORE: 0.53744 at epoch 3\n",
      "\n",
      "***** (4/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 64, 'lr': 0.001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00009: early stopping\n",
      "SCORE: 0.55437 at epoch 4\n",
      "\n",
      "***** (5/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 32, 'lr': 0.0001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00027: early stopping\n",
      "SCORE: 0.56897 at epoch 22\n",
      "\n",
      "***** (6/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 32, 'lr': 0.0001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00033: early stopping\n",
      "SCORE: 0.58825 at epoch 28\n",
      "\n",
      "***** (7/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 32, 'lr': 0.001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00013: early stopping\n",
      "SCORE: 0.53967 at epoch 8\n",
      "\n",
      "***** (8/16) *****\n",
      "Search({'units_gru': 128, 'units_cnn': 32, 'lr': 0.001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00009: early stopping\n",
      "SCORE: 0.5806 at epoch 4\n",
      "\n",
      "***** (9/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 64, 'lr': 0.0001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00027: early stopping\n",
      "SCORE: 0.54722 at epoch 22\n",
      "\n",
      "***** (10/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 64, 'lr': 0.0001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00049: early stopping\n",
      "SCORE: 0.55186 at epoch 44\n",
      "\n",
      "***** (11/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 64, 'lr': 0.001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00008: early stopping\n",
      "SCORE: 0.55204 at epoch 3\n",
      "\n",
      "***** (12/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 64, 'lr': 0.001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00020: early stopping\n",
      "SCORE: 0.52201 at epoch 15\n",
      "\n",
      "***** (13/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 32, 'lr': 0.0001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00032: early stopping\n",
      "SCORE: 0.59309 at epoch 27\n",
      "\n",
      "***** (14/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 32, 'lr': 0.0001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00049: early stopping\n",
      "SCORE: 0.60055 at epoch 44\n",
      "\n",
      "***** (15/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 32, 'lr': 0.001, 'batch_size': 512})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00017: early stopping\n",
      "SCORE: 0.52293 at epoch 12\n",
      "\n",
      "***** (16/16) *****\n",
      "Search({'units_gru': 64, 'units_cnn': 32, 'lr': 0.001, 'batch_size': 1024})\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00020: early stopping\n",
      "SCORE: 0.54354 at epoch 15\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'units_gru': [128, 64],\n",
    "    'units_cnn': [64, 32],\n",
    "    'lr': [1e-4, 1e-3],\n",
    "    'batch_size': [512, 1024]\n",
    "}\n",
    "\n",
    "es = EarlyStopping(patience=5, verbose=1, min_delta=0.001, monitor='val_loss', mode='auto', restore_best_weights=True)\n",
    "kgs = KerasGridSearch(get_model, param_grid, \n",
    "                      monitor='val_loss', greater_is_better=False, tuner_verbose=1)\n",
    "kgs.search(X_train, y_train_seq, validation_data=(X_val, y_val_seq), callbacks=[es], epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:18:58.218705Z",
     "iopub.status.busy": "2021-04-15T10:18:58.218014Z",
     "iopub.status.idle": "2021-04-15T10:18:59.518593Z",
     "shell.execute_reply": "2021-04-15T10:18:59.520030Z"
    },
    "papermill": {
     "duration": 1.338609,
     "end_time": "2021-04-15T10:18:59.520320",
     "exception": false,
     "start_time": "2021-04-15T10:18:58.181711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### MAKE PREDICTION ON TEST ###\n",
    "\n",
    "pred = reverse_target(kgs.best_model.predict([X_test]).ravel(), mean_train, std_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:18:59.650129Z",
     "iopub.status.busy": "2021-04-15T10:18:59.646892Z",
     "iopub.status.idle": "2021-04-15T10:18:59.944547Z",
     "shell.execute_reply": "2021-04-15T10:18:59.943430Z"
    },
    "papermill": {
     "duration": 0.365497,
     "end_time": "2021-04-15T10:18:59.944693",
     "exception": false,
     "start_time": "2021-04-15T10:18:59.579196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PREDICTIONS vs REALITY ON TEST ###\n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "start, end = 2100, 3000\n",
    "plt.plot(test_date[sequence_length+start:end+sequence_length], y_test[sequence_length+start:end+sequence_length], \n",
    "         c='orange', label='real test')\n",
    "plt.plot(test_date[sequence_length+start:end+sequence_length], pred[start:end], \n",
    "         c='green', label='prediction')\n",
    "plt.legend(); plt.ylabel('PM2.5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.034499,
     "end_time": "2021-04-15T10:19:00.015091",
     "exception": false,
     "start_time": "2021-04-15T10:18:59.980592",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# INFERENCE ON PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:19:00.097801Z",
     "iopub.status.busy": "2021-04-15T10:19:00.096965Z",
     "iopub.status.idle": "2021-04-15T10:19:00.101903Z",
     "shell.execute_reply": "2021-04-15T10:19:00.101211Z"
    },
    "papermill": {
     "duration": 0.051905,
     "end_time": "2021-04-15T10:19:00.102038",
     "exception": false,
     "start_time": "2021-04-15T10:19:00.050133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTIONS FOR GRADIENT IMPORTANCE AND ACTIVIATION MAPS GENERATION ### \n",
    "\n",
    "\n",
    "def gradient_importance(seq, model):\n",
    "\n",
    "    seq = tf.Variable(seq[np.newaxis,:,:], dtype=tf.float32)\n",
    "\n",
    "    with tf.GradientTape() as tape:\n",
    "        predictions = model(seq)\n",
    "\n",
    "    grads = tape.gradient(predictions, seq)\n",
    "    grads = tf.reduce_mean(grads, axis=1).numpy()[0]\n",
    "    \n",
    "    return grads\n",
    "\n",
    "\n",
    "def activation_grad(seq, model):\n",
    "    \n",
    "    seq = seq[np.newaxis,:,:]\n",
    "    grad_model = Model([model.inputs], \n",
    "                       [model.get_layer('extractor').output, \n",
    "                        model.output])\n",
    "\n",
    "    # Obtain the predicted value and the intermediate filters\n",
    "    with tf.GradientTape() as tape:\n",
    "        seq_outputs, predictions = grad_model(seq)\n",
    "\n",
    "    # Extract filters and gradients\n",
    "    output = seq_outputs[0]\n",
    "    grads = tape.gradient(predictions, seq_outputs)[0]\n",
    "\n",
    "    # Average gradients spatially\n",
    "    weights = tf.reduce_mean(grads, axis=0)\n",
    "    \n",
    "    # Get a ponderated map of filters according to grad importance\n",
    "    cam = np.ones(output.shape[0], dtype=np.float32)\n",
    "    for index, w in enumerate(weights):\n",
    "        cam += w * output[:, index]\n",
    "\n",
    "    time = int(seq.shape[1]/output.shape[0])\n",
    "    cam = zoom(cam.numpy(), time, order=1)\n",
    "    heatmap = (cam - cam.min())/(cam.max() - cam.min())\n",
    "    \n",
    "    return heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:19:00.198402Z",
     "iopub.status.busy": "2021-04-15T10:19:00.196451Z",
     "iopub.status.idle": "2021-04-15T10:19:00.438440Z",
     "shell.execute_reply": "2021-04-15T10:19:00.438962Z"
    },
    "papermill": {
     "duration": 0.301357,
     "end_time": "2021-04-15T10:19:00.439153",
     "exception": false,
     "start_time": "2021-04-15T10:19:00.137796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### DISPLAY INPUT SEQUENCE ###\n",
    "\n",
    "id_ = 128\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "plt.plot(X_test[id_])\n",
    "plt.ylabel('std series'); plt.xlabel('time lags'); plt.legend(columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:19:00.519548Z",
     "iopub.status.busy": "2021-04-15T10:19:00.518495Z",
     "iopub.status.idle": "2021-04-15T10:19:01.092916Z",
     "shell.execute_reply": "2021-04-15T10:19:01.093527Z"
    },
    "papermill": {
     "duration": 0.61695,
     "end_time": "2021-04-15T10:19:01.093878",
     "exception": false,
     "start_time": "2021-04-15T10:19:00.476928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### GRADIENTS IMPORTANCE ###\n",
    "\n",
    "grad_imp = gradient_importance(X_test[id_], kgs.best_model)\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.bar(range(len(grad_imp)), grad_imp)\n",
    "plt.xticks(range(len(columns)), columns, rotation=90)\n",
    "plt.ylabel('gradients'); plt.title(pd.to_datetime(test_date[sequence_length+id_]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-15T10:19:01.178238Z",
     "iopub.status.busy": "2021-04-15T10:19:01.177253Z",
     "iopub.status.idle": "2021-04-15T10:19:01.544996Z",
     "shell.execute_reply": "2021-04-15T10:19:01.545523Z"
    },
    "papermill": {
     "duration": 0.414574,
     "end_time": "2021-04-15T10:19:01.545688",
     "exception": false,
     "start_time": "2021-04-15T10:19:01.131114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### GENERATE ACTIVATION ON INPUT SEQUENCE ###\n",
    "\n",
    "activation = activation_grad(X_test[id_], kgs.best_model)\n",
    "\n",
    "plt.figure(figsize=(9,5))\n",
    "plt.plot(X_test[id_])\n",
    "plt.ylabel('std series'); plt.xlabel('time lags'); plt.title(pd.to_datetime(test_date[sequence_length+id_]))\n",
    "plt.twinx()\n",
    "plt.imshow(np.vstack([activation]*30), alpha=0.35)\n",
    "plt.axis('off'); plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 216.978651,
   "end_time": "2021-04-15T10:19:04.224107",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-04-15T10:15:27.245456",
   "version": "2.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
